{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1c510dda",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "da181486",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=range(-10,10,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "49da1a77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e2bf8fc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y1=[xi**3 for xi in x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0da30012",
   "metadata": {},
   "outputs": [],
   "source": [
    "y2=[xi**2 for xi in x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e939e731",
   "metadata": {},
   "outputs": [],
   "source": [
    "y3=[200]*len(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2c352323",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26011c08ee0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y1)\n",
    "plt.plot(x,y2)\n",
    "plt.plot(x,y3)\n",
    "plt.xlabel(\"iteration\",fontsize=20)\n",
    "plt.ylabel(\"xnew\",fontsize=18)\n",
    "plt.legend([\"x ^3\",\"x ^2\",\"y=10\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4426fd4f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
